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1、An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis 1. INTRODUCTIONThe power transformer is a major apparatus in a power system, and its correct functioning its vital to minimize system outages, many devices have evolved to monitor the serviceability of power transformers.
2、These devices, such as, Buchholz relays or differential relays, respond only to a severe power failure requiring immediate removal of the transformer from service, in which case, outages are inevitable. Thus, preventive techniques for early detection faults to avoid outages would be valuable. In thi
3、s way, analysis of the mixture of the faulty gases dissolved in insulation oil of power transformer has received worldwide recognition as an effective method for the detection of oncipient faults. Many researchers and electrical utilities have reported on their experience and developed interpretativ
4、e criteria on the basis of DGA. However, criteria tend to vary from utility to utility. Therefore, transformer diagnosis is still in the heuristic stage. For this reason, knowledge-based programming is a suitable approach to implement in such a diagnostic problem.Based on the interpretation of DGA,
5、a prototype of an expert system for diagnosis of suspected transformer faults and their maintenance procedures is proposed. The significant source in this knowledge base is the gas ratio method. Some limitations of this approach are overcome by incorporating the diagnostic procedure and the syntheti
6、c expertise method. Furthermore, data bases adopted from TPCS gas records of transformers are incorporated into the expert system to increase the practical performance. Uncertainty of diagnosis is managed by using fuzzy set concepts. This expert system is constructed with rule based knowledge repres
7、entation, since it can be expressed by experts. The expert system building tool,knowledge Engineering System(KES), is used in the development of the knowledge system because, it has excellent man-machine interface that provides suggestions. Moreover,its inference strategy is similar to the MYCIN. A
8、famous rule-based expert system used for medical diagnosis. The uncertainty of human qualitative diagnostic expertise, e.g., key gas analysis, and another quantitative imprecision, such as, norms threshold and gas ratio boundaries etc., are smoothed by appropriate fuzzy models. With the results of s
9、uch implementation, different certainty factors will be assigned to the corresponding expertise variables. Both event-driven(forward chaining) and goal-driven (backward chaining) inferences are used in the inference engine to improve the inference efficiency. To demonstrate the feasibility of the pr
10、oposed expert system, around hundreds of TPC historical gas records have been tested. It is found that more appropriate faulty types and maintenance suggestions can support the maintenance personals to increase the performance of transformer diagnosis. 2. DEVELOPMENT OF DIAGNOSIS AND INTERPRETATION
11、Like many diagnostic problems, diagnosis of oil-immersed power transformer is a skilled task. A transformer may function well externally with monitors, while some incipient deterioration may occur internally to cause a fatal problem in the latter development. According to a Japanese experience, near
12、ly 80% of all faults result from incipient deteriorations. Therefore, faults should be identified and avoided at the earliest possible stage by some predictive maintenance technique. DGA is one of the most popular techniques for this problem. Fault gases in transformers are generally produced by oil
13、 degradation and other insulating material, e.g., cellulose and paper. Theoretically, if an incipient or active fault is present, the individual dissolved gas concentration, gassing rate, total combustible gas(TCG) and cellulose degradation are all significantly increased. By using gas chromatograph
14、y to analyse the gas dissolved in a transformers insulating oil, it becomes feasible to judge the incipient fault types. This study is concerned with the following representative combustible gases; hydrogen(H2), methane(C2H2), ethane(C2H6), ethylene(C2H2) and carbon monoxide(C0).Many interpretative
15、methods based on DGA to the nature of incipient deterioration have been reported. Even under normal transformer operational conditions, some of these gases may be formed inside. Thus, it is necessary to build concentration norms from a sufficiently large sampling to assess the statistics. TPC invest
16、igated gas data from power transformers to construct its criteria. The developed knowledge base in this paper is partially based on these data. On the hand, Dornerburg developed a method to judge different faults by rating pairs of concentrations of gases, e.g., CH/H, GH/C3H4, with approximately equ
17、al solubility and fusion coefficients. Rogers established mare comprehensive ratio codes to interpret the thermal fault types with theoretical thermodynamic assessments. This gas ratio method was promising because it eliminated the effect of oil volume and simplified the choice of units. Moreover, i
18、t systematically classified the diagnosis expertise in a table form. Table 1 displays the ratio method as proposed by Rogers. The dissolved gas may vary with the nature and severity of different faults. By analyzing the energy density of faults, its possible to distinguish three basic fault processe
19、s:overheating(pyrolysis), corona(partial dischatge) and arcing discharge. Corona and arcing arise from electrical faults, while overheating is a thermal fault. Both types of faults my lead to deterioration, while damage from overheating is typically less than that from electrical stress. Infect, dif
20、ferent gas trends lead to different faulty types, the key gas method is identified. For example, large amounts of CH and H are produced with minor arcing fault 4 quantities of CH 2aid C2H2 may be a symptom of an arcing fault.3. THE PROPOSED DIAGNOSTIC EXPERT SYSTEM This study is aimed at developing
21、a rule-based expert system to perform transformer diagnosis similar to a human expert. The details of system processing are described below.3.1 The Proposed Diagnostic Method Diagnosis is a task that requires experience. It is unwise to determine an approach from only a few investigations. Therefore
22、, this study uses the synthetic expertise method with the experienced procedure to assist the popular gas ratio method and complete practical performance.3.1.1 Experienced Diagnostic ProcedureThe overall procedure of routine maintenance for transformers is listed. The core of this procedure is based
23、 on the implementation of the DGA technique. The gas ratio method is the significant knowledge source. Some operational limitations of the gas ratio method exist. The ratio table is unable to cover all possible cases. Minimum levels of gases must be present. The solid insulation involving CO and CO
24、are handled separately and the gas ratio codes have been developed mainly from a free-breathing transformer. Other diagnostic expertise should be used to assist this method. Norms, synthetic expertise method and data base records have been incorporated to complete these limitations. The first step o
25、f this diagnostic procedure begins by asking DGA for an oil sample to be tested. More important relevant information about the transformers condition, such as the voltage level, the preservative type, the on-line-tap-changer(OLTC) state, the operating period and degassed time must be known for furth
26、er inference. Norms(criteria) Set up by TPC power transformers gas characteristic data are then used to judge the transformers condition. For the abnormal cases, the gas ratio method is used to diagnose transformer fault type. If different or unknown diagnosis results are found from these ratio meth
27、ods, a further synthetic expertise method is adopted. After these procedures, different severity degrees are assigned to allow appropriate corresponding maintenance suggestions.3.1.2 Synthetic Expertise MethodThe ratio trend, norms threshold, key gas analysis and some expertise are considered as dif
28、ferent evidences to confirm some special fault types. In other words, more significant evidences have been collected for some special fault type, better assessment of the transformer status is obtained.The ratio trend can be seen as a modification of the conventional gas ratio and key gas method.Obv
29、iously, the above gas trends should be incorporated with other evidences under the experienced procedure for practical use. Norms threshold, the gassing rate, the quantity of total combustible gas(TCG), the TPC maintenance expertise and the fuzzy set assignment are all important evidences considered
30、 in the synthetic diagnosis.Other expertise based on a transformer historical data base is also used to analyse the characteristics of a case transformer. Section 3.4 gives some details of these rules.3.2 Expert System StructureThe proposed diagnostic expert system is composed of components, working
31、 memory, a knowledge base, an inference engine and a man-machine interface. Working memory (global data base) contains the current data relevant to solve the present problem. In this study, most of the diagnostic variables stored in the data base are current gas concentration, some are from the user
32、, others are retrieved from the transformers historical data base. Note that the fuzzy set concept is incorporated to create fuzzy variables on the request of system reasoning. A knowledge relationship, which uses these facts, as the basis for decision making. The production rule used in this system
33、 is expressed in IF-THEN forms. A successful expert system depends on a high quality knowledge base. For this transformer diagnostic system, the knowledge base incorporates some popular interpretative methods of DGA, synthetic expertise method and heuristic maintenance rules. Section 3.4 will descri
34、be this knowledge base. Another special consideration in the expert system is its inference engine. The inference engine controls the strategies of reasoning and searching for appropriate knowledge. The reasoning strategy employs both forward chaining(data-driven) and backward chaining(goal-driven).
35、 Fuzzy rules, norms rules, gas ratio rules, synthetic expertise rules and some of the maintenance rules and some maintenance rules, use forward chaining.As for the searching strategy in KES, the depth first searching and short-circuit evaluation are adopted. The former can improve the search efficie
36、ncy by properly arranging the location of significant rules in the inference procedures. The latter strategy only searches the key conditional statements in the antecedent that are responsible for establishing whether the entire rule is true or false. Taking the advantages of these two approaches in
37、 the building and structuring of a knowledge base improves inference efficiency significantly.As for man-machine interface. KES has an effective interface which is better than typical knowledge programming languages, such as, PROLOG or LISP. With the help of this interface, the capability of tracing
38、, explaining and training in an expert system is greatly simplified.4. IMPLEMENTATION OF THE PROPOSED EXPERT SYSTEM An expert system is developed based on the proposed interpretative rules and diagnostic procedures of the overall system. To demonstrate the feasibility of this expert system in diagno
39、sis, the gas data supported by MTL of TPC have been tested. In Taiwan, the MTL of TPC performs the DGA and sends the results to all acting divisions relating to power transformers. In return, these acting divisions are requested to collect and supply their transformer oil samples periodically. After
40、 analysing oil samples, more than ten years worthy gas records are collected and classified into three voltage level, 69KV, 16KV and 345KV. Thus, gas records for one transformer are composed of several groups of data. In the process of DGA interpretation, all of these data may be considered, but onl
41、y the recent data which have significant effects on diagnosis are listed in the later demonstration. In MTL, all gas concentrations are expressed by pm in volume concentration. 100 pm is equal to 0.01 ml(gas)/100ml(oil).From the expertise of diagnosis, the normal state can be confirmed only by inspe
42、ction of the transformers norms level. In practice, most of the transformer oil samples are normal, and this can be inferred successfully on the early execution of this expert system. However, the Success of an expert system is mainly dependent on the capability of diagnosis for the transformers in
43、question. In the implementation, many gas records which are in abnormal condition are chosen to test the Justification of this diagnostic system. A total of 101 transformer records have been executed and the results are summarized in Table 5. Among those implemented, three are listed and demonstrate
44、d.Shown in Table 5 are the results of 101 units of transformers in three types of remedy: normal, thermal fault and arc fault. After comparing them with the actual state and expert judgement, a summary of results was obtained. As previously stated, one unit of transformer may include many groups of
45、gas data. In evaluation, we depicted some key groups in one unit to justify because some transformers may have different incipient faults during different operational stages. Some mistakes implemented from testing are caused by the remaining oil in the oil sampling container, unstable gas characteri
46、stics of the new degassing sample and some obscure gas types. If more information or new techniques support other uncertain membership functions, they can be added into the knowledge has to enlarge the the performance of this prototype expert system. Furthermore, the parameters described in table 2,
47、3 and 4 are suitable for TPC power transformer. Different regions may be modified the maintenance personnel find more suitable system parameters.5. CONCLUSIONSA prototype expert system is developed on a personal computer using KES. It can diagnose the incipient faults of the suspected transformers a
48、nd suggest proper maintenance actions. Fuzzy set concept is used to handle uncertain norms thresholds, gas ratio boundaries and key gas analysis. The synthetic method and diagnostic procedure are proposed to assist the situation which can not be handled properly by the gas ratio methods. Results fro
49、m the implementation of the expert system shows that the expert system is a useful tool to assist human expert and maintenance engineers. The knowledge base of this expert system is incorporated within the popular interpretative method of DGA, synthetic expertise and heuristic maintenance rules. The data base supported by TPC MTL for about 10 year collection of transformer inspection data is also used to improve the interpretation of diagnosis. Through the development of the proposed expert system, the expertise